ADAM FINN*
An information processing perspective is used to develop hierarchical and divergent models of how individuals process print ads. An aggregation across individuals
generated related audience-level models, which were operationalized by using Starch
scores and extended to incorporate specific ad charocteristics. Confirmatory tests
indicate that these models provide a substantial advance over previous data-driven
approaches to analyzing readership scares.
Print Ad Recognition Readership Scores: An
Information Processing Perspective
As reported by Starch (1966). research on the effectiveness of print ads began around 1900. The early use
of ads in memory experiments soon evolved into systematic recognition and recall procedures for testing the
effectiveness of print ads. Recognition measures of print
ad readership first were provided commercially by Gallup and then by Daniel Starch, who founded the current
syndicated service in 1932. The continued success of this
service and recent validation research (Zinkham and Gelb
1986) both indicate that recognition readership scores
provide useful information about the effectiveness of ads.
Starch not only provides its well-known "noted," "associated," and "read most" scores, but also reports on
the noting of component parts of larger ads (Starch 1966,
p. 12). Normally included are a "seen" score for the
major illustration, a "noted" score for the signature, and
a "read some" score for the major block of copy.
The modem era of academic investigations of the impact of print ad characteristics on recognition readership
scores was ushered in by Twedt (1952). Since then, academic and proprietary studies have been done over and
over again (Neu 1983). Typically, the Starch scores for
a sample of ads are regressed on a set of coded ad characteristics. Causa! relationships are assumed to underlie
statistically significant ad characteristics, so normative
conclusions are drawn, ultimately to be used in guidelines for the design of print advertising (Stansfield 1969).
Despite the number of such studies, several concerns
can be raised about this stream of data-driven research.
First, it has been challenged on the grounds that recognition scores are neither valid nor reliable measures of
print ad effectiveness (Clancy, Ostlund, and Wyner 1979;
Singh and Cole 1985). Second, without a conceptual
model of the relationship between the various Starch
scores, the scores always have been analyzed alone, not
simultaneously. More powerful analytical techniques have
not been used. Finally, there has been an overreliance
on exploratory procedures and modest ad sample sizes,
without recognition of the danger of drawing causal inferences (Armstrong 1970).
A new approach to the relationship between print ad
characteristics and recognition readership scores is described in the following sections. First, the nature of ad
readership scores is reconsidered, with the suggestion that
they be viewed from an information processing perspective. This perspective now dominates theoretical research on advertising effects (Alwitt and Mitchell 1985)
and is of practical relevance because information processing concepts parallel the sequence of decisions advertisers make (Shimp and Gresham 1983). Information
processing theory then is used to develop hierarchical
and divergent models of how an individual processes a
print ad. By aggregation across individuals, these microlevel models generate related audience-level models,
which are operationalized by using Starch scores. A review of prior readership research is used to incorporate
specific ad characteristics into these models. Confirmatory tests of the models then are conducted on data
*Adam Finn is Assistant Professor, Department of Marketing and
Economic Analysis, Faculty of Business, University of Alberta.
The author expresses his appreciation to Starch-INRA-Hooper, Inc.
for kindly providing access to the data for the study, and to Roy Howeil, Ruth Bolton, Jordan Louviere. and several anonymous JMR reviewers for their useful comments and suggestions.
16B
Journal of Marketing Research
Vol. XXV (May 1988). 168-77
PRINT AD RECOGNITION SCORES
obtained from Starch-INRA-Hooper. Finally, the implications of the research are discussed.
NATURE OE PRINT AD RECOGNITION SCORES
Once the regular testing of print ads was accepted in
the 1950s, a controversy arose over the relative merits
of recognition and aided recall testing of print ads. Recognition scores usually were several times aided recall
scores. In 1955 the Advertising Research Foundation
conducted a field experiment to compare recognition,
aided recall, and reader interest scores for ads in an issue
of Life magazine. This experiment, commonly referred
to as the PARM study, found that recognition scores were
very reliable and, at least in the short term, did not decline with the passage of time from exposure to the ads.
In contrast, aided recall scores were less reliable and declined quickly with time from exposure.
The observed reliability and stability of recognition
scores troubled many observers. First, if recognition scores
were measures of memory, they should fade, not remain
stable. Second, recognition studies of magazines including bogus ads (Marder and David 1961) and comparisons of claimed noting with observed exposure (Lucas
1960) both confirmed that many individuals falsely claim
recognition. Research on this false claiming found that
it increased with interest in the product being advertised
(Appel and Blum 1961). that it increased with deviations
from the Starch interview procedure (Starch 1966, Ch.
3), and that social desirability effects accounted for only
a small proportion of false claims (Clancy, Ostlund, and
Wyner 1979).
One consequence of these concerns has been suggestions for improving recognition methodology, including
the use of control groups (Appel and Blum 1961) and
forced-choice recognition methods (Singh and Churchill
1986). A second result has been a claim that recognition
scores are a biased measure of memory. Bagozzi and
Silk (1983) present the most sophisticated development
of this view in their reanalysis of the PARM data. After
controlling for a "biasing" effect of interest in the ad,
they found recognition "noted" scores to be very reliable
measures of the cognitive memory for ads.
However, this may not be the only reasonable conceptualization of recognition scores. They may be indicative of how an ad has been processed by an audience. First, Starch labels its service as a readership, not
a memory, report. It attempts to measure what people
observe and read during consumption of print media
(Starch 1966, p. 10). Second, Gallup was looking for
an objective measure of interest in printed material (Lucas and Britt 1963. p. 57) and assumed that readership
under natural exposure conditions would reflect the degree of interest. Hence, it is not surprising that Wells
(1964) found both reader interest in and ratings of the
attractiveness of ads to be correlated strongly with noting scores and that Bagozzi and Silk (1983) found the
interest confound. Finally, in evaluating the PARM study,
Lucas (I960) concluded that recognition scores were "a
169
rough indicator of reader behavior, perhaps a guide to
some kind of psychological contact more substantive than
mere visual exposure."
Here, an individual's recognition responses are assumed to reflect three components. One is the processing
that the ad actually received. The second is systematic
error due to such effects as the decay in memory of that
processing and the tendency to report how an ad would
have been processed. The third is random measurement
error. Both random and systematic error can lead to false
claiming by individuals. Even if the proportion of individual random error variance is high, however. Starch
scores stiil would be expected to be very reliable because
Starch aggregates across the responses of about 100 individuals. The real issue is the validity of Starch scores
when used in cross-sectional research on ad effectiveness. The systematic bias in Starch scores due to memory effects was shown to be modest by Bagozzi and Silk
(1983). Systematic bias due to yea-saying reports of how
an ad would have been processed should be correlated
with the aggregate processing the ad actually received.
It would inflate scores, but it should not be a significant
problem in cross-sectional studies of characteristics contributing to the effectiveness of print ads.
CONSUMER INFORMATION PROCESSING
OE A PRINT AD
The development of a model of print ad readership
begins at the micro level with individual information
processing. This approach has several advantages. By
interrelating the various readership responses, a tnicrolevel model identifies how aggregate readership scores
are interrelated and so should be analyzed together. In
contrast, prior research has been ad hoc. Researchers have
either ignored all but one score (Bagozzi and Silk 1983;
Sparkman 1985), as though the scores were equivalent,
or treated readership scores independently, as though they
were unrelated (Hanssens and Weitz 1980; Holbrook and
Lehmann 1980; Rossiter 1981). A micro-level mode! also
provides a framework for structuring an assessment of
prior research. Finally, it can help to identify other ad
characteristics that influence readership scores.
The information processing literature supports both hierarchical and divergent models of the processing a print
ad receives from an individual. Both models begin with
the assumptions that consumers act to accomplish their
own goals and have limited processing capacity. As a
result, capacity is devoted to available information to the
extent that it is believed likely to facilitate attainment of
those goals (Bettman 1979, p. 2).
Hierarchical Processing Model
The hierarchical model is a strictly bottom-up processing interpretation of the four levels of audience involvement in advertising suggested by Greenwald and
Leavitt (1984). The successive levels of preattention, focal attention, comprehension, and elaboration require increasing processing capacity. In strictly bottom-up pro-
170
JOURNAL OF AAARKETING RESEARCH, MAY 1988
cessing, if processing at one level fails to evoke the next
highest level, processing of the ad is terminated and the
capacity is allocated to some other task.
Beginning at the bottom level, a consumer is exposed
to an ad because of its size and location within a print
vehicle. This preattention processing enables the consumer to determine that the ad is not a continuation of
what was being processed previously. The consumer can
terminate processing at this level, in which case the ad
produces little or no enduring effect, or can continue to
process the ad in a manner that will contribute most efficiently to achieving his or her goals. This processing
is done at the focal attention level, requiring just enough
additional capacity to determine what the ad is about.
Eye movement studies indicate this step is achieved most
economically by processing the pictorial material first.
About 90%.of viewers fixate the dominant picture in an
ad before they fixate the copy (Kroeber-Riel 1984). The
enduring effect of focal attention is the formation of a
visual image of the ad, making subsequent recognition
possible.
If the visual image generated by focal attention evokes
the higher comprehension level of involvement, the consumer analyzes the image to form propositions assigning
it meaning. The enduring effects are propositional traces
in memory. Finally, if the highest elaboration level of
involvement is evoked, the consumer's full capacity is
used to respond cognitively to the ad, generating personal connections and imagery. The enduring effect is a
conceptual integration of the meaning found in the ad
with prior knowledge. The hierarchical model is represented by the continuous lines in Figure 1.
Figure 1
TWO MODELS OF INDIVIDUAL INFORA\ATION
PROCESSING OF A PRINT AD
Advertisement
Characterfslics
Information Processing
Constructs
Enduring
Effects
Ad size and
location in a
print vehicle
Littie
or none
Layout of ad
and dominant
illustration
Image
formation
Secondary
elements ol
ad: Headline,
logo a other
illustrations
Proposition
formation
Detail of ad
copy, etc.
Integration with
existing knowledge
Divergent Processing Model
Though accepting that the dominant pictorial material
in an ad is processed first, Edell and Staelin (1983) offer
an altemative model. They argue that any farther processing is conditional on the framing of the ad, the consistency between its pictorial material and verbal messages. They suggest that framing is required for elaborative
processing to occur, in effect rejecting the strictly hierarchical model in favor of a divergent processing model,
indicated by the broken lines in Figure I.
Mitchell (1983) proposed that once a print ad has received attention, subsequent processing depends on the
goals of the individual. If the goal is to evaluate the
product suggested by the picture, the individual will form
verbal representations and engage in elaboration. If other
goals are dominant, the consumer will not engage in verbal product-related elaborations. Instead, the visual secondary elements of the ad will be processed to comprehend further the pictorial material. Different individuals
may process the same ad differently.
AUDIENCE READERSHIP OF PRINT ADS
To generate audience-level readership models that are
consistent with the micro models requires an aggregation
Two Model Hierarchial;
variants:
Divergent,
across consumers, from an individual to an audience level.
The simplest approach to this aggregation is to assume
homogeneity. With this stringent assumption, the hierarchical and divergent models yield analogous audiencelevel models. Relaxing the homogeneity assumption introduces more complexity, so only the simplest form of
heterogeneity is considered here. If some individuals
process hierarchically and others divergently, aggregation will produce a mixed model at the audience level.
Hence, hierarchical, divergent, and mixed audience-level
models are consistent with current consumer information
processing theories.
The audience-level constructs in these models are designated as exposure received (ExpR), attention received
(AttR), comprehension attained (CompA), and elaboration attained (ElabA). Starch scores, which aggregate over
individuals, are at the right level to provide measures of
these constructs. The constructs can be linked to particular Starch scores by their differential enduring effects.
The enduring effect of focal attention is the formation
171
PRINT AD RECOGNITION SCORES
of a visual image of the ad, so recognition of ad pictorial
content is indicative of processing at the focal-attention
level (Greenwald and Leavitt 1984, p. 590). Thus Starch
noted and seen scores appear to be indicators of AttR.
Noted scores often have been referred to as measures of
the attention-getting power of ads (Diamond 1968; Valiente 1973; Wells 1964).
The enduring effect of comprehension is the formation
of propositional traces assigning meaning to the ad, so
recognition of such propositions is indicative of processing at the comprehension level (Greenwald and Leavitt
1984). Because a link to a sponsor is the most universal
of such propositions, recognition of the sponsor's name
and signature may be indicative of comprehension-level
processing. If so. Starch associated and signature scores
appear lo be suitable indicators of CompA.
The enduring effect of elaboration is an integration of
ad meaning with prior knowledge, so free recall of ad
content is indicative of such processing (Greenwald and
Leavitt 1984). Starch does not measure recall, but read
most and read some scores may be an acceptable alternative. Elaboration requires additional processing time
and these scores indicate the duration of processing. Read
most scores correlate strongly with ad reading times
measured by means of an eye camera (Starch 1966, p.
26). When raised, read most scores always have been
considered indicative of a higher level of readership involvement than either noted or associated scores (Rossiter 1981; Valiente 1973).
With little or no enduring effect of preattention, there
is no retrospective indicator of ExpR. Therefore, as shown
in Figure 2, ExpR must be omitted from the models to
be estimated. This omission is not expected to bias the
assessment of the effects of ad characteristics on AttR,
because the models including ExpR are all recursive
(Blalock 1982, Ch. 5).
In Figure 2, theoretical constructs are represented as
ellipses and their measures are represented as rectangles.
Specifically, TI, represents AttR, 1^2 represents CompA,
and T), represents ElabA; K, through Yf, are the Starch
score indicators of the constructs. The differences between the models are in the structural relations. For the
hierarchical model, p.,, for the direct causal path from
AttR to ElabA would be zero. For the divergent model,
P32 for the causal path from CompA to ElabA would be
zero. For the mixed model, all three structural parameters would be positive and significant.
Comparing the fit of these models does not allow the
drawing of defmitive inferences about how individuals
process ads, because one cannot distinguish between
heterogeneity and altemative models. The same mixed
model is obtained if every individual can process both
hierarchically and divergently or if groups of individuals
process each way. However, these models can be compared with baseline models representing prior conceptualization in the field (Sobel and Bohmstedt 1985) to
determine whether the information processing perspective adds insight at the audience level. Several research-
Figure 2
EXTENDED MODEL OF THE AUDIENCE READERSHIP
OF PRINT ADS
Advertise men 1
Characteristics
Audience Readership
Constructs
Starch Score
Irtdlcators
Ad Size
Front/Back
Cover
Facing Ads
Right/Left
Color
Illustration Size
Photo/Art
Bleed
Residual
ers have analyzed noted, associated, and read most scores
independently (Hanssens and Weitz 1980; Holbrook and
Lehmann 1980; Rossiter 1981), implicitly modeling the
scores as measures of three unrelated constructs. Other
researchers have analyzed only the noted scores (Bagozzi and Silk 1983; Sparkman 1985), implicitly modeling all the scores as indicators of a single construct.
Reconsideration of Prior Research
•'
To incorporate specific ad characteristic effects into
these audience models, the findings of prior readership
research were reconsidered from an information processing perspective. That view suggests ad size and locational characteristics should have an indirect impact on
AttR through their effect on ExpR. Layout and pictorial
characteristics should have a direct impact on AttR and,
through it, weaker indirect effects on CompA and ElabA.
Secondary ad elements should influence CompA and copy
characteristics should influence ElabA, but a failure to
control for AttR would make identifying such effects difficult. The direct effect of an ad characteristic on CompA
(ElabA) would be identifiable only by markedly different effects on AttR and CompA (ElabA).
Table I summarizes the ad characteristic fmdings re-
172
JOURNAL OF MARKETING RESEARCH, AAAY 1988
Table 1
FREQUENCY OF SIGNIFICANT FINDINGS IN PRIOR RESEARCH RELATING AD
CHARACTERISTICS TO PRINT AD READERSHIP SCORES
Ad characteristics
Size and location
Ad size
Front/back pages
Cover position
Facing: ad/editorial
Right/left page
Layout and pictorial
Color
Illustration size
Photo/art
Bleed/no bleed
Other characteristics
Headline:
Words
Phrases
Nouns
Verbs
Adjectives
Determiners
Type size
Product reference
Personal reference
Question form
Benefit
Product as object
Copy:
Amount
Readability
Benefits
+
AttR indicator'
No. of findings'^
ns
14
9
4
2
2
0
2
1
2
2
13
8
5
5
0
4
2
5
0
0
0
2
0
1
1
0
0
0
0
0
2
0
0
0
8
1
1
1
2
2
6
4
0
3
4
2
0
0
0
I
0
0
0
1
0
0
I
0
0
1
I
1
0
2
2
6
4r
0
0
0
4
2
3
2
0
CompA indicator
No. of findings
0
0
0
0
1
0
1
ElabA indicator'
No. of findings
+
ns
X
4
0.
3
0
•1
2
0
1
0
0
0
6
3
0
0
0
1
4
3
1
3
0
0
0
0
0
3
2
1
3
1
2
0
0
0
5
2
1
I
5
6
6
5
0
0
0
1
0
0
0
1
0
0
2
I
0
0
0
0
0
0
6
2
3
3
I
2
4
5
3
3
4
2
1
0
0
0
0
0
1
1
0
0
0
0
4
0
0
0
1
0
4
1
1
2
0
2
3
0
1
0
0
I
0
2
0
0
I
2
3
1
3
2
2
2
0
0
1
1
0
®
0
0
0
0
0
0
0
1
0
1
'Starch "noted" but includes some Ad-Chart "noticed" results.
"Mainly Starch "associated."
•^Mainly Starch "read most" but includes some Ad-Chart "read half or more."
"Plus sign indicates positive and significant, ns indicates not significant, and minus sign indicates negative and sigtiificant.
ported in the modern academic literature.' Five size and
location characteristics have had consistently significant
effects on AttR, slightly less consistent effects on CompA,
and still less consistent effects on ElabA. In order of
strength of evidence, these characteristics are ad size,
front rather than back pages, a cover position, facing
other ads rather than editorial, and right rather than left
page. They are grouped in Figure 2, as their effects on
AttR are assumed to be indirect through ExpR. Four pictorial characteristics have had the same pattern of ef-
'Included are ad characteristics for which significant effects were
reported in studies by Ferguson (1935). Warner and Franzen (1947),
Twedt (1952). Anderson (I960), Gardner (1961). Frankel and Soiov
(1962), Yamanaka (1962), Gardner and Cohen (1964), Troldahl and
Jones (1965). Assael, Kofron, and Burgi (1967). Myers and Haug
(1967), Tumbull and Carter (1968). Diamond (1968), Valiente (1973),
Fletcher and Winn (1974), Surlin and Kosak (1975), Holbrook and
Lehmann (1980), Hanssens and Weitz (1980), Rossiter (1981), Soley
and Reid (1983a.b), Blunden, Clarke, and MacDougal! (1984). and
Spaiicman (1985). Though most of these researchers used Starch scores,
some used similar scores from Ad-Chart (Stansfield 1969, p. 1232).
fects, consistent with direct effects on AttR. In order of
strength of evidence, they are color, illustration size, photo
rather than drawn illustration, and bleed. Evidence is insufficient to justify confirmatory testing of other characteristics effects.
CONFIRMATORY TESTS
Data
Starch-INRA-Hooper provided the Starch score data
for 1981 issues oi Business Week (December 28), Reader's Digest (August), Hot Rod (May), and Family Circle
(October 13) in response to a request for access to data
for four different magazines. These magazines yielded a
total of 266 Starch score observations for full-page or
larger ads, including both male and female scores for the
45 ads in Reader's Digest. As Starch scores are essentially proportions, with many observations in the lower
quartile, the data were subjected to a tail-stretching arcsine transformation (transformed score = 20 arcsin [square
root (Starch score/100)]) that stabilizes their variance
(Cohen and Cohen 1975, p. 254). The covariances, vari-
173
PRINT AD RECOGNITION SCORES
Table 2
VARIANCE-COVARIANCE A N D CORRELATION MATRICES
FOR TRANSFORMED STARCH SCORES'
Y,
Y.
Y,
Y,
Y.
Y,
Noted
Seen
Y,
Y^
9.226
.999
.956
.944
.716
.683
9.263
9.316
.956
.946
.712
.683
Associated
Y,
8.578
8.619
8.723
.989
.703
.674
Signature
Read
some
Y,
8.117
8.173
8.266
8.010
.696
.664
5.168
5.169
4.939
4.683
5.653
.919
Read
most
Ye
4.760
4.781
4.566
4.313
5.012
5.264
'Correlations below, variances along, and covariances above the
diagonal.
ances, and correlations for these transformed scores are
reported in Table 2.^
Operational measures of the ad characteristics employed in the study were chosen by considering the definitions used in prior research, while recognizing the need
to control for the number and size of pages in the four
magazines. Each ad was coded independently for the following characteristics by two judges. Possible discrepancies were resolved by going back to the ad (e.g., cover
position, left/right). The final inter-rater Pearson or
Spearman correlation coefficients are reported in parentheses.
Ad size. Coded as I for a full-page ad, 2 for a doublepage spread, and 3 for gatefoids and other ads of a full
page or larger accompanied by inserts (.89).
Color. Treated as an interval variable, with black and
white ads coded as 1. black and white plus a single color
as 2, black and white plus two colors as 3, and full color
as four (.98).
Front/back. After adjustment to include inserted alphabetical sections, the page on which the ad appeared was
divided by the total number of pages in each magazine
and the resulting proportion was subjected to the arcsine
transformation (1.0).
Illustration size. Combined photo and illustration space
in the ad as a proportion of the page size of the magazine
(.71).
Photo or art. The number of photos in the ad divided by
the total number of illustrations, giving a proportion that
was subjected to the arcsine transformation (.89).
Cover position. A O-l variable with 1 for any one of the
inside front, inside back, or outside back covers (1.0).
Left/right. A 0-1 variable coded as I for a right page
location (1.0).
'These correlations are higher than expected; the noted and associated correlation of .95 exceeds tbe recent reports of .87 (Rossiter
1981) and 83 (Zinkham and Gelb 1986). However, the correlation
pattem is consistent with tbe proposed correspondence rules. Covariance data were used for model estimation.
Facing ad/editorial. A 0-1 variable with 1 for ads facing
editorial pages (1.0).
Bleed. A 0-1 variable with 1 indicating the use of bleed
(1.0).
Estimation of the Starch Scores Relationship Models
Because of the complexity of the full model in Figure
2, the models of the relationship between Starch scores
were evaluated before the extended model was estimated
(Bagozzi 1983). To identify the models, the structural
relation residuals for AttR (Ci). CompA (^2). and ElabA
(l,i) were assumed uncorrelated and X,. \ , . and \f, were
fixed at 1, making the scales for the AttR (TI,), CompA
(T)2), and ElabA (TI,) respectively the same as Chose for
the transformed noted (/,), signature (K^), and read most
(Yf,) scores. To help evaluate the fit of these models, a
null model (Bentler and Bonett 1980) and the two prior
practice baseline models also were estimated. For the
first baseline model, the structural parameters were all
fixed at zero to give three unrelated constructs. For the
second, all six Starch scores were modeled as indicators
of a single construct.
The fit indices obtained from LISREL VI maximum
likelihood estimation of these models are reported in Table 3. All models failed to fit according to the chi square
likelihood ratio criterion, a statistic known to be sensitive to violations of the distributional assumptions and
to give infiated rejections of a known model at this sample size (Boomsma 1982). However, judged against the
simulation results reported by Anderson and Gerbing
(1984), all three information-processing-based models
provide a reasonably good fit to the data. In addition,
they all provide a highly satisfactory improvement in fit
over both of the baseline models.^ The mixed and divergent models are superior to the hierarchical model on
all fit indices. As the mixed model is the more general
'Against the better fitting one-common-conslruci baseline model,
tbe degree of freedom adjusted (p) and unadjusted (A) fit coefficients
of Sobel and Bohmstedl (1985) for the hierarchical, divergent, and
mixed models were respectively p = .921, A = .930; p = .935, A =
.941; and p = .927. A = .944.
Table 3
FIT OF MODELS FOR TRANSFORMED STARCH DATA
Model
d.f.
Null (uncorrelated
15
random variables)
Baseline (three unrelated
9
constructs)
Baseline (one common
9
construct)
Discriminant (combine
8
AttR & CompA)
7
Hierarchical
7
Divergent
6
Mixed
Model ftt statistics
P 8-fi- a.gf.i.
x'
rmr
3894.2 .000 .226
-.083 5.540
846.7 .000 .591
.(M7 4.707
749.5 .000 .656
.198
.538
436.9
52.3
44.3
41.9
.409
.823
.850
.835
.163
.122
.058
.005
.000
.000
.000
.000
.775
.941
.950
.953
174
JOURNAL OF MARKETING RESEARCH, AAAY 1988
Table 4
PARAMETER ESTIAAATES FOR AUDIENCE READERSHIP OF PRINT ADS MODELS
Model parameter
Noted-AttR
Seen-AttR
Signature-CompA
Associated-CompA
Read some-ElabA
Read most-ElabA
AtlR-CompA
AttR-ElabA
CompA-ElabA
Residual of AttR
Residual of CompA
Residual of ElabA
Ad size-AttR _
Front/back-AttR
Cover-AttR
Facing ads-AttR
Right/left page-AttR
Color-AttR
Illustration size-AttR
Photo/art-AltR
Uses bleed-AttR
Front/back-ElabA
Color-ElabA
Unique noted
Unique seen
Unique signature
Unique associated
Unique read some
Unique read most
Mixed
relationship
model: ML'
Symbol
K
Xi
,
9»
'I'l
«h
•
l.ff
1.005
1.0^
1.056
1.084
I.O'
.881
.356
.182
9.222
.672
2.143
(.003)
(.010)
(.039)
(.019)
(.108)
(.116)
(.822)
(.064)
(.224)
7L3
7M
•Yis
7l6
7l7
-ym
Extended deierminant modets
Original: ML'
1.0"
1.004 (.003)
l.O'
1.056 (.010)
1.084 (.039)
- l.O^
.880 (.019)
.358 (.107)
.180 (.116)
5.286 (.471)
.675 (.064)
2.142 (.224)
1.354 (.668)
.589 (.207)
1.902 (.754)
-.881 (.602)
.776 (.343)
.807 (.144)
2.778 (.688)
• -.038 (.164)
-.334 (.378)
TfM
•y»
«€„
9*44
ec.6
.004 (.007)
.011 (.007)
.183 (.028)
-.006(025)
.222 (.138)
.638 (.130)
.003
.012
.185
-.008
.220
.640
(.007)
(.007)
(.028)
(.025)
(.138)
(.130)
Modified: ML'
1.0'
1.004 (.003)
I.O^
1.056 (.010)
1.106 (.039)
1.0^
.880 (.019)
.466 (.101)
.152 (.107)
5.286 (.471)
.676 (.065)
1.878 (.197)
1.355 (.668)
.589 (.207)
1.902(754)
-.881 (.602)
.776 (.343)
.807 (.144) •;
2.780 (.688)
-.038 (.164)
-.334 (.378)
-.451 (.126)
-.384 (.081)
.003 (.007)
.012 (.007)
.187 (.028)
-.010 (.025)
.108 (.127)
.733 (.123)
Modified: UV
1.0'
1.005
l.O'
1.056
1.082
1.0"^
.880
.428
.187
5.429
.680
1.945
1.731
.491
1.423
-.968
.701
.837
2.369
-.002
-.323
-.447
-.310
.004
.001
.183
-.008
.232
.630
Fit parameters
41.88 (6 d.f.)
.953
g.f.i.
a.g.f.i.
.83*
rmr
'Maximum likelihood parameter (standard error in parentheses)
"Unweighted least squares parameter.
'Parameter fixed to equal 1.
.tm
model, it is used in subsequent analysis.
The parameter values and standard errors for the mixed
model are reported in the first column of Table 4. The
presence of a small negative variance for the associated
uniqueness (6644) was cause for some concern. However, compared with a standard error of .025, the estimate of - . 0 0 6 is small enough to be ascribed to sampling fluctuations when the true variance is positive but
close to zero (van Driel I978).'' The AttR-ElabA (pa,)
and AttR-CompA (pjj) structural parameter values are
both highly significant, explaining the better fit of the
'Clever respecifications have been proposed for such improper solutions (Rindskopf 1983), but bave proved of little practical value
(Dillon. Kumar, and Mulani 1987). Fixing the offending variance to
zero or a small positive value also adds little to the interpretability of
the improper solution, wbich can be evaluated for goodness offitby
using the indices provided by the LISREL program (Gerbing and Anderson 1987).
201.7 (51 d.f.)
.907
:066
171.1 (49 d.f.)
.919
.801
.037
L(HX)
LOOO
.028
divergent model. The CompA-ElabA (p^j) parameter is
not quite significant, explaining why the mixed model
gives only a marginally better fit.
Substantively, differences in AttR account for about
91% of the variance in CompA and more than 53% of
the variance in EtabA. Indeed, the strong AttR-CompA
(P21) relationship could be viewed as calling into question the discriminant validity of these two constructs. To
assess this possibility, a model treating the noted, seen,
associated, and signature scores as indicators of one construct was estimated. As shown in Table 3, this model
is an improvement over the baseline models, but all three
of the information-processing-based models are a substantial further improvement over it.^
'The fit coefficients for the one-common-construct baseline model
were only p = .348 and A = .417. whereas the fit coefficients were
p = .888 and A = .904 for tbe mixed model and p = .901 and A =
.898 for tbe divergent model.
PRINT AD RECOGNITION SCORES
Estimation of the Extended Model
Many of the ad characteristics in the extended model
are nominal or ordinal variables, raising questions about
the use of the maximum likelihood estimation (Joreskog
and Sorbom 1984, Ch. 4). A check of the normality of
the conditional distribution of each transformed Starch
score for each level of these fixed-effect independent
variables found normality was not rejected at the .05 level
by a Kolmogorov-Smimov test. However, normality was
rejected at the . 10 level for the read most indicator. Hence,
maximum likelihood estimation was employed to make
the fullest use of the available data and to be able to
assess the significance of the structural parameters, but
with recognition that the program standard errors and chi
square test would be sensitive to departures for normality.
The maximum likelihood parameter and standard error
estimates for the extended model are reported in column
2 of Table 4. Six of the nine ad characteristics have the
expected signs and parameter values that are approximately two or more times their standard errors. In order
of significance, these characteristics are color, illustration size, front/back, cover position, right page, and ad
size. The modest impact of ad size may be due to the
truncation of the sizes used in the study. The other characteristics have the wrong signs, but are not significant.
Together the nine ad characteristics account for 42% of
the variance in AttR. As is desirable, the other structural
parameters and measurement relations remain virtually
unchanged. However, the modification indices provided
by the program indicate that for color and for a front
location, the indirect effect through AttR does not account adequately for the total effect on ElabA. As reported in the third column of Table 4, a modified model
found their positive effects on AttR to be accompanied
by negative direct effects on ElabA. These direct effects
increase the variance in ElabA accounted for from 53.7
to 58.5%. Though potentially of substantive importance,
these effects on ElabA were not hypothesized and require future confirmation.
The modified model also was estimated by using unweighted least squares, which can be justified without
the distributional assumptions of maximum likelihood
(Joreskog and Sorbom 1984). As shown in Table 4, this
procedure produced a stronger effect for ad size and a
weaker effect for cover position without changing the
substantive conclusions.^
DISCUSSION AND IMPLICATIONS
Adoption of an information processing perspective and
aggregation across individuals generated hierarchical,
divergent, and mixed models of how an audience processes a print ad. Proposed links between the constructs
in these audience models and particular Starch scores en-
"Similar results were obtained by using other estimation methods.
Of tbese. generalized least squares in EQS (Bentler 1985) gave proper
solutions for all models in Table 4.
175
abled the models to be estimated with Starch data. All
three information-processing-based models provided a
substantially better fit to the data than did baseline mtxiels
representing other views of readership scores. Thus, there
is confirmation for modeling Starch scores as indicators
of three interrelated processing constructs.
The divergent and the mixed models provided a better
fit than the hierarchical model. Estimation of the extended mixed model confirmed that four size and location (ad size, cover position, front or back of the vehicle,
right or left page) and two pictorial characteristics (color
and illustration size) were significant determinants of AttR.
Limitations
The implications that can be drawn from the results
depend on the validity of the proposed links between individual information processing constructs and Starch responses. Starch associated and signature responses tap
only one aspect of comprehension and Starch read some
and read most responses were substituted for recall responses. A study of the relationship between level of
involvement, type of processing, recall, and recognition
responses to print ads is needed to resolve the validity
issue. If the proposed links are not valid, the constructs
underlying Starch scores would have to be reinterpreted
at a more operational level. Though there would be little
consequence for the practical implications of the research, any information processing interpretation of the
results would be eliminated.
.
Implications for Print Advertisers
The results explain the continued practitioner focus on
noted scores. For this sample of ads, differences in the
AttR by the ads account for more than 90% of the variance in the CompA and more than half of the variance
in the ElabA.
Substantively, the results suggest that the AttR by print
ads is determined largely by some now well-established
location and illustration characteristics. Of these, only
one, illustration size, is both under the direct creative
control of the ad designer and not already subject to a
premium. Therefore this finding is the most clearly actionable. In addition, to the extent that they have a choice,
print advertisers should attempt to obtain right-page locations toward the front of magazines rather than left
pages toward the back. Allocating the maximum possible proportion of ad space to pictorial material is likely
to be optimal if one wants to ensure CompA and is probably optimal if one is seeking ElabA. Finally, though
premiums for cover and color remain justified, after controlling for illustration size, the premium for bleed may
not be justified.
Implications for Future Ad Readership Research
Future ad readership research should be conducted
within the framework of a specific model, such as the
mixed processing model presented here. Exploratory research is needed to identify other characteristics that influence ad processing. The greatest need is for the iden-
176
JOURNAL OF MARKETING RESEARCH, MAY 1988
tification of additional determinants of AttR. The
identification of determinants of ElabA, given AttR, is
a second priority. Research on the determinants of
CompA, after controlling for AttR. can produce only
marginal gains. Simple analytical procedures such as
stepwise regression can continue to be used, but only
after controlling for previously confirmed relationships.
Exploratory research on AttR must control for the six
confirmed characteristics. Similar research on ElabA must
control for the strong effects of AttR.
An information processing perspective should help to
identify ad characteristics to include in this research. Additional layout and illustration characteristics should be
considered as determinants of AttR. Details of ad copy,
such as sentence structure, might be expected to influence ElabA directly. Similarly, aspects of the ad signature might be expected to affect CompA.
Further confirmatory research is needed to generalize
the results obtained here and to test exploratory findings
such as the negative effects of color and front location
on ElabA found here. The power of tests of the effects
of particular characteristics can be increased significantly by controlling for previously established relationships.
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